FEM analyses for influences of stress corrosion and pressure solution on THM coupling in dual-porosity rock mass

With the models of stress corrosion and pressure solution, by Yasuhara et al., two computation conditions were designed for a hypothetical nuclear waste repository in an unsaturated dual-porosity rock mass: (i) the fracture apertures are changed with the stress corrosion and pressure solution (the porosity of rock matrix is also a function of stress); (ii) the fracture apertures and the porosity of rock matrix are constants. Then the corresponding two-dimensional FEM analyses for the coupled thermohydro-mec...     

Evolutionary digital twin: A new approach for intelligent industrial product development

To fulfill increasingly difficult and demanding tasks in the ever-changing complex world, intelligent industrial products are to be developed with higher flexibility and adaptability. Digital twin (DT) brings about a possible means, due to its ability to provide candidate behavior adjustments based on received “feedbacks” from its physical part. However, such candidate adjustments are deterministic, and thus lack of flexibility and adaptability. To address such problem, in this paper an extended concept – evolutionary digital twin (EDT) and an EDT-based new mode for intelligent industrial product development has been proposed. With our proposed EDT, a more precise approximated model of the physical world could be established through supervised learning, based on which the collaborative exploration for optimal policies via parallel simulation in multiple cyberspaces could be performed through reinforcement learning. Hence, more flexibility and adaptability could be brought to industrial products through machine learning (such as supervised learning and reinforcement learning) based self-evolution. As a primary verification of the effectiveness of our proposed approach, a case study has been carried out. The experimental results have well confirmed the effectiveness of our EDT based development mode.     

A systematic data-mining-based methodology for product family design and product configuration

Product family design and product configuration based on data mining technology is identified as an intelligent and automated means to improve the efficiency of product development. However, few of previous literatures have proposed systematic product family design method based on data mining technology. To make up for this deficiency, this research put forward a systematic data-mining-based method for product family design and product configuration. First, the customer requirement information and product engineering information in the historical order are formatted into structural data. Second, principal component analysis is performed on historical orders to extract the customers' differentiated needs. Third, association rule algorithm is introduced to mine the rules between differentiated needs and module instances in the historical orders, thus obtained the configuration knowledge between customer needs and product engineer. Forth, the mined rules are used to construct association rule-based classifier (CBA) that is employed to sort out the best product configuration schemes as popular product variants. Fifth, sequence alignment technique is employed to identify modules for popular product variants, so that the module instances are divided into optional, common and special module, respectively, thereby the product platform is generated based on common modules. Finally, according to new customer needs, the CBA classifier is used to recommend the best configuration schemes, and then popular product variants are configured based on the product platform. The feasibility of the proposed method is demonstrated by the product family design example of desktop computer hosts.     

Visitor-artwork network analysis using object detection with image-retrieval technique

Recent museum exhibitions are becoming a means by which to satisfy visitor demands. In order to provide visitor-centric exhibitions, artwork must be analyzed based on the behavior of visitors, and not merely according to museum professionals' points of view. This study aims to analyze the relationship between museum visitors and artwork via a network analysis based on visitor behavior using object detection techniques. Cameras installed in a museum recorded visitors, and an object detector with a content-based image-retrieval technique tracked visitors from the videos. The durations spent with different artworks were measured, and the data was converted into a bipartite graph. The relationships between different artwork types were analyzed with a visitor-centered artwork network. Based on the visitors’ behavior, significant artworks were identified and the artwork network was compared to the arrangement of the museum. The tendency of edges in the artwork network was also examined considering visitors' preferences for artworks. The method used here makes it possible to collect quantitative data, with the results possibly used as a basis and for reference when analyzing artwork in a visitor-centered approach.     

A synthetical development approach for rehabilitation assistive smart product–service systems: A case study

Digital transformation (DT), the combination of information, computing, communication and connectivity technologies, which has triggered an effective upgrade of different aspects of market strategy, customer experience etc. Nowadays, rehabilitation assistive devices (RADs) are evolving to be more digital, intelligent and personalized. Digitalization and servitization have fostered to an emerging business model—the smart product–service system (Smart PSS). Therefore, DT of the RADs’ industry advocates not only the design of products and functions, the more important is the management of service processes and resource integration. With the increase in the elderly and disabled population, the requirement for RADs is becoming more urgent. However, research on Smart PSS for RAD is still limited. The rehabilitation assistive smart product–service systems (RASPSS) was introduced into the development of RADs based on the “Design and Management of DT” strategy through the service design of assistive devices and user requirements analysis. Further, an integrated design of RAD and Smart PSS has been created, a development method of RASPSS proposed, the theoretical model of the Smart PSS based on RADs built. To specify the service framework, this case study discusses the development of a home rehabilitation assistive system for femoral stem fracture patients. This paper evaluates the usability of the system, the results of which prove usability and effectiveness of the RASPSS development method. The RASPSS development model is designed to meet needs of stakeholders, improve the user rehabilitation experience, promote the service innovation of Smart PSS, bring certain market benefits of rehabilitation aids and create social value.     

Smart dispatching and optimal elevator group control through real-time occupancy-aware deep learning of usage patterns

Passengers spend much time on elevator journeys in high-rise buildings every day, in which unnecessary stops caused by lack of cab capacity take up a certain proportion of the journey time. This study proposes real-time occupancy-aware smart dispatching to avoid pick-up failure by introducing the occupancy information that reflects elevator capacity into the optimization model, thus improving dispatching performance. Occupancy awareness is firstly implemented with deep learning-based object detection to provide estimated capacity. Traffic pattern recognition is implemented with time series analysis and fuzzy logic. Case-based reasoning is applied to recognize the current usage pattern and to deploy specific dispatching strategies. A prioritized A* search model is built to solve dispatching optimization with occupancy information. Discrete event simulation is conducted with Simio and MATLAB to validate the proposed dispatching model.     

Autoencoder-based anomaly detection for surface defect inspection

In this paper, the unsupervised autoencoder learning for automated defect detection in manufacturing is evaluated, where only the defect-free samples are required for the model training. The loss function of a Convolutional Autoencoder (CAE) model only aims at minimizing the reconstruction errors, and makes the representative features widely spread. The proposed CAE in this study incorporates a regularization that improves the feature distribution of defect-free samples within a tight range. It makes the representative feature vectors of all training samples as close as possible to the mean feature vector so that a defect sample in the evaluation stage can generate a distinct distance from the trained center of defect-free samples. The proposed CAE model with regularizations has been tested on a variety of material surfaces, including textural and patterned surfaces in images. The experimental results reveal that the proposed CAE with regularizations significantly outperforms the conventional CAE for defect detection applications in the industry.     

Influence of depth-thickness ratio of mining on the stability of a bedding slope with its sliding surface in concave deformation

In order to study the influence of depth-thickness ratio on bedding slope stability, whose sliding surface is flexural concave in shape under mining conditions, this paper aims to study the characteristics of deformation and damage of bedding sliding with depth-thickness ratios of 200:1, 150:1, 120:1, 100:1 and 50:1by adopting numerical simulation analysis software combined with laboratory-made ‘‘under the influence of mining variable sliding surface slope similar simulation test bed", and to propose identification methods for slope stability under the influence of mining. The results show that mining activities under the slope reduce slope stability. With a decrease in the mining depth ratio, the influence of mining on the slope increases gradually, and the damage to the slope gradually expands, the stability of the slope gradually reduces, fracture occurs on the slope toe and the central fissure gradually develops to the surface,and reaches slide threshold when the depth-thickness ratio is 50:1.     

Non-Fourier heat conduction in a thin gold film heated by an ultra-fast-laser

Based on the dual-phase-lagging(DPL)heat conduction model,the Cattaneo-Vernotte(CV)model and the improved CV model we investigate the one-dimensional heat conduction in gold films with nano-scale thickness exposed to an ultra-fast laser heating.The influence of system parameters on the temperature field is explored.We find that for all the non-Fourier heat conduction models considered in this work,a larger Knudsen number normally leads to a higher temperature.For the DPL model,the large ratio of the phase lag of temperature gradient to the phase lag of heat flux reduces the maximum temperature and shortens the time for the system to reach its steady state.The CV model and the improved CV model lead to the similar thermal wave behavior of the temperature field,but the thermal wave speeds for these two models are different,especially for large Knudsen numbers.When the phase lag of temperature gradient is smaller,the difference between the DPL model and the improved CV model is not significant,but for the large phase lag of temperature gradient the difference becomes quite significant,especially for the large Knudsen number.In addition,the effect of the surface accommodation coefficient,which is a parameter in the slip boundary condition,on the temperature field of the gold film heated by ultra-fast laser pulses is investigated based on the DPL model.